ACTA THERIOLOGICA SINICA ›› 2023, Vol. 43 ›› Issue (6): 734-744.DOI: 10.16829/j.slxb.150774
• REVIEWS • Previous Articles Next Articles
ZHONG Junjie1, NIU Bing1, CHEN Qin1, CHEN Xiang2, WANG Yan3
Received:
2023-02-08
Revised:
2023-07-14
Online:
2023-11-30
Published:
2023-11-22
钟俊杰1, 钮冰1, 陈沁1, 陈翔2, 王艳3
通讯作者:
王艳, E-mail:289315233@qq.com
作者简介:
钟俊杰(2000-),男,硕士研究生,主要从事保护生物学研究.
基金资助:
CLC Number:
ZHONG Junjie, NIU Bing, CHEN Qin, CHEN Xiang, WANG Yan. Application of deep learning in wildlife conservation[J]. ACTA THERIOLOGICA SINICA, 2023, 43(6): 734-744.
钟俊杰, 钮冰, 陈沁, 陈翔, 王艳. 深度学习在野生动物保护中的应用[J]. 兽类学报, 2023, 43(6): 734-744.
Al Bashit A, Valles D. 2019. MFCC-based Houston Toad call detection using LSTM. 2019 IEEE International Symposium on Measurement and Control in Robotics (Ismcr): Robotics for the Benefit of Humanity. DOI: 10. 1109/ismcr47492. 2019. 8955667 Aldausari N, Sowmya A, Marcus N, Mohammadi G. 2023. Video generative adversarial Networks: A review. Acm Computing Surveys, 55 (2). DOI: 10. 48550/arXiv. 2011. 02250 Alom M Z, Taha T M, Yakopcic C, Westberg S, Sidike P, Nasrin M S, Hasan M, Van Essen B C, Awwal A A S, Asari V K. 2019. A State-of-the-Art survey on deep learning theory and architectures. Electronics, 8 (3). DOI: 10. 3390/electronics8030292 Alzubaidi L, Zhang J L, Humaidi A J, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaria J, Fadhel M A, Al-Amidie M, Farhan L. 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). DOI: 10. 1186/s40537-021-00444-8 Antoniadis A, Lambert-Lacroix S, Poggi J M. 2021. Random forests for global sensitivity analysis: A selective review. Reliability Engineering & System Safety, 206. DOI: 10. 1016/j. ress. 2020. 107312 Bergler C, Schmitt M, Cheng R X, Schroter H, Maier A, Barth V, Weber M, Noth E. 2019. Deep representation learning for orca call type classification. Text, Speech, and Dialogue (Tsd 2019), 11697: 274-286. Bonte C, Vercauteren F. 2018. Privacy-preserving logistic regression training. BMC Medical Genomics, 11 (S4). DOI: 10. 1186/s12920-018-0398-y Chen P, Swarup P, Matkowski W M, Kong A W K, Han S, Zhang Z H, Rong H. 2020. A study on giant panda recognition based on images of a large proportion of captive pandas. Ecology and Evolution, 10 (7): 3561-3573. Christin S, Hervet E, Lecomte N. 2019. Applications for deep learning in ecology. Methods in Ecology and Evolution, 10 (10): 1632-1644. Dai W, Wang H P, Fan C S, Xin Y W. 2021. Object Segmentation based on Anti-disturb Network in Ecological Surveillance. Proceedings of the 33rd Chinese Control and Decision Conference(Ccdc 2021). DOI: 10. 1109/Ccdc52312. 2021. 9602523 Dargan S, Kumar M, Ayyagari M R, Kumar G. 2020. A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4): 1071-1092. de Silva E M K, Kumarasinghe P, Indrajith K, Pushpakumara T V, Vimukthi R D Y, de Zoysa K, Gunawardana K, de Silva S. 2022. Feasibility of using convolutional neural networks for individualidentification of wild Asian elephants. Mammalian Biology, 102(3): 909-919. Eikelboom J A J, Wind J, van de Ven E, Kenana L M, Schroder B, de Knegt H J, van Langevelde F, Prins H H T. 2019. Improving the precision and accuracy of animal population estimates with aerial image object detection. Methods in Ecology and Evolution, 10(11): 1875-1887. Falzon G, Lawson C, Cheung K W, Vernes K, Ballard G A, Fleming P J S, Glen A S, Milne H, Mather-Zardain A, Meek P D. 2020. ClassifyMe: A Field-Scouting software for the identification of wildlife in camera trap images. Animals, 10 (1). DOI: 10. 1101/ 646737 Gibb R, Browning E, Glover-Kapfer P, Jones K E. 2019. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution, 10(2): 169-185. Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. 2014. Generative adversarial Nets. Advances in Neural Information Processing Systems 27(Nips 2014), 27: 2672-2680. Guo S T, Xu P F, Miao Q G, Shao G F, Chapman C A, Chen X J, He G, Fang D Y, Zhang H, Sun Y W, Shi Z H, Li B G. 2020a. Automatic identification of individual primates with deep learning techniques. iScience, 23 (8): 101412. Guo Y H, Rothfus T A, Ashour A S, Si L, Du C L, Ting T F. 2020b. Varied channels region proposal and classification Network for wildlife image classification under complex environment. Iet Image Processing, 14 (4): 585-591. Hah J, Lee W, Lee J, Park S. 2018. Information-based boundary equilibrium generative adversarial Networks with interpretable representation learning. Computational Intelligence and Neuroscience, 2018. DOI: 10. 1155/2018/6465949 Hassell J M, Begon M, Ward M J, Fevre E M. 2017. Urbanization and disease emergence: Dynamics at the wildlife-livestock-human interface. Trends in Ecology & Evolution, 32 (1): 55-67. Hayes M C, Gray P C, Harris G, Sedgwick W C, Crawford V D, Chazal N, Crofts S, Johnston D W. 2021. Drones and deep learning produce accurate and efficient monitoring of large-scale seabird colonies. Ornithological Applications, 123 (3). DOI:10. 1093/ornithapp/duab022 Hinton G E, Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313 (5786): 504-507. Hou J, He Y X, Yang H B, Connor T, Gao J, Wang Y J, Zeng Y C, Zhang J D, Huang J Y, Zheng B C, Zhou S Q. 2020. Identification of animal individuals using deep learning: A case study of giant panda. Biological Conservation, 242. DOI: 10. 1016/j. biocon. 2020. 108414 Islam S B, Valles D. 2020. Identification of wild species in texas from camera-trap images using deep neural network for conservation monitoring. 2020 10th Annual Computing and Communication Workshop and Conference (Ccwc), 537-542. Janiesch C, Zschech P, Heinrich K. 2021. Machine learning and deep learning. Electronic Markets, 31 (3): 685-695. Jeantet L, Hadetskyi V, Vigon V, Korysko F, Paranthoen N, Chevallier D. 2022. Estimation of the maternal investment of sea turtles by automatic identification of nesting behavior and number of eggs laid from a Tri-Axial accelerometer. Animals, 12 (4). DOI:10. 3390/ani12040520 Khamparia A, Singh K M. 2019. A systematic review on deep learning architectures and applications. Expert Systems, 36 (3). DOI:10. 1111/exsy. 12400 Kim J I, Baek J W, Kim C B. 2022. Image classification of amazon parrots by deep learning: a potentially useful tool for wildlife conservation. Biology-Basel, 11 (9). DOI: 10. 3390/biology11091303 Lei Y J, Dong P M, Guan Y, Xiang Y, Xie M, Mu J, Wang Y Z, Ni Q Y. 2022a. Postural behavior recognition of captive nocturnal animals based on deep learning: a case study of Bengal slow loris. Scientific Reports, 12 (1). DOI: 10. 1038/s41598-022-11842-0 Lei Y J, Xiang Y, Zhu Y H, Guan Y, Zhang Y, Yang X, Yao X L, Li T X, Xie M, Mu J, Ni Q Y. 2022b. Development of a slow loris computer vision detection model. Animals, 12: 1553. Li J, Xu C, Jiang L X, Xiao Y, Deng L M, Han Z Z. 2020. Detection and analysis of behavior trajectory for sea cucumbers based on deep learning. IEEE Access, 8: 18832-18840. Li J, Xu W K, Deng L M, Xiao Y, Han Z Z, Zheng H Y. 2022. Deep learning for visual recognition and detection of aquatic animals: A review. Reviews in Aquaculture. DOI: 10. 1111/raq. 12726 Liu J, Wang X W. 2021. Plant diseases and pests detection based on deep learning: A review. Plant Methods, 17 (1). DOI: 10. 1186/s13007-021-00722-9 Lu T, Han B K, Yu F Q H. 2021. Detection and classification of marine mammal sounds using AlexNet with transfer learning. Ecological Informatics, 62. DOI: 10. 1016/j. ecoinf. 2021. 101277 Madhusudhana S, Shiu Y, Klinck H, Fleishman E, Liu X B, Nosal E M, Helble T, Cholewiak D, Gillespie D, Sirovic A, Roch M A. 2021. Improve automatic detection of animal call sequences with temporal context. Journal of the Royal Society Interface, 18(180). DOI: 10. 1098/rsif. 2021. 0297 Maekawa T, Ohara K, Zhang Y, Fukutomi M, Matsumoto S, Matsumura K, Shidara H, Yamazaki S J, Fujisawa R, Ide K, Nagaya N, Yamazaki K, Koike S, Miyatake T, Kimura K D, Ogawa H, Takahashi S, Yoda K. 2020. Deep learning-assisted comparative analysis of animal trajectories with DeepHL. Nature Communications, 11 (1). DOI: 10. 1038/s41467-020-19105-0 Malhi Y, Lander T, le Roux E, Stevens N, Macias-Fauria M, Wedding L, Girardin C, Kristensen J A, Sandom C J, Evans T D, Svenning J C, Canney S. 2022. The role of largewild animals in climate change mitigation and adaptation. Current Biology, 32 (4): R181-R196. Mao A X, Huang E D, Wang X S, Liu K. 2023. Deep learning-based animal activity recognition with wearable sensors: Overview, challenges, and future directions. Computers and Electronics in Agriculture, 211. DOI: 10. 1016/j. compag. 2023. 108043 Mishra D, Singh S K, Singh R K. 2022. Deep architectures for image compression: A critical review. Signal Processing, 191: 108346. Mittal R, Arora S, Bansal V, Bhatia M P S. 2021. An extensive study on deep learning: techniques, applications. Archives of Computational Methods in Engineering, 28 (7): 4471-4485. Nanni L, Costa Y M G, Aguiar R L, Mangolin R B, Brahnam S, Silla C N. 2020. Ensemble of convolutional neural networks to improve animal audio classification. Eurasip Journal on Audio Speech and Music Processing, 2020 (1). DOI: 10. 1186/s13636-020-00175-3 Nguyen H, Maclagan S J, Nguyen T D, Nguyen T, Flemons P, Andrews K, Ritchie E G, Phung D. 2017. Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. 2017 IEEE International Conference on Data Science and Advanced Analytics (Dsaa). DOI: 10. 1109/Dsaa. 2017. 31: 40-49 Permana S D H, Saputra G, Arifitama B, Yaddarabullah, Caesarendra W, Rahim R. 2022. Classification of bird sounds as an early warning method of forest fires using Convolutional Neural Network (CNN) algorithm. Journal of King Saud UniversityComputer and Information Sciences, 34 (7): 4345-4357. Petso T, Jamisola R S, Mpoeleng D. 2022. Review on methods used for wildlife species and individual identification. European Journal of Wildlife Research, 68 (1). DOI: 10. 1007/s10344-021-01549-4 Phelps J, Webb E L, Bickford D, Nijman V, Sodhi N S. 2010. Boosting CITES. Science, 330 (6012): 1752-1753. Piccinini G. 2004. The first computational theory of mind and brain:A close look at McCulloch and Pitts's“logical calculus of ideas immanent in nervous activity”. Synthese, 141 (2): 175-215. Porkodi S P, Sarada V, Maik V, Gurushankar K. 2022. Generic image application using GANs (Generative Adversarial Networks): A review. Evolving Systems. DOI: 10. 1007/s12530-022-09464-y Roy A, Fablet R, Bertrand S L. 2022. Using generative adversarial networks (GAN) to simulate central-place foraging trajectories. Methods in Ecology and Evolution, 13 (6): 1275-1287. Ruff Z J, Lesmeister D B, Appel C L, Sullivan C M. 2021. Workflow and convolutional neural network for automated identification of animal sounds. Ecological Indicators, 124. DOI: 10. 1016/j.ecolind. 2021. 107419 Rumelhart D E, Hinton G E, Williams R J. 1986. Learning representations by back-propagating errors. Nature, 323 (6088): 533-536. Santangeli A, Chen Y X, Boorman M, Ligero S S, Garcia G A. 2022. Semi-automated detection of tagged animals from camera trap images using artificial intelligence. Ibis, 164 (4): 1123-1131. Schindler F, Steinhage V. 2021. Identification of animals and recognition of their actions in wildlife videos using deep learning techniques. Ecological Informatics, 61. DOI: 10. 1016/j. ecoinf. 2021. 101215 Schmidhuber J. 2015. Deep learning in neural networks: An overview. Neural Networks, 61: 85-117. Segebarth D, Griebel M, Stein N, von Collenberg C R, Martin C, Fiedler D, Comeras L B, Sah A, Schoeffler V, Luffe T, Durr A, Gupta R, Sasi M, Lillesaar C, Lange M D, Tasan R O, Singewald N, Pape H C, Flath C M, Blum R. 2020. On the objectivity, reliability, and validity of deep learning enabled bioimage analyses. Elife, 9. DOI: 10. 7554/eLife. 59780 Shi C M, Liu D, Cui Y L, Xie J J, Roberts N J, Jiang G S. 2020. Amur tiger stripes: individual identification based on deep convolutional neural network. Integrative Zoology, 15 (6): 461-470. Shi C M, Xu J, Roberts N J, Liu D, Jiang G S. 2022. Individual automatic detection and identification of big cats with the combination of different body parts. Integrative Zoology. DOI: 10. 1111/ 1749-4877. 12641 Shrestha R, Glackin C, Wall J, Cannings N. 2021. Bird Audio Diarization with Faster R-CNN. Artificial Neural Networks and Machine Learning-Icann 2021, Pt I, 12891: 415-426. Sun Y H, Mu Y, Feng Q, Hu T L, Gong H, Li S J, Zhou J. 2020. Deer body adaptive threshold segmentation algorithm based on color space. CMC-Computers Materials & Continua, 64 (2): 1317-1328. Surya T, Selvi S C, Selvaperumal S. 2022. The IoT-based real-time image processing for animal recognition and classification using deep convolutional neural network (DCNN). Microprocessors and Microsystems, 95. DOI: 10. 1016/j. micpro. 2022. 104693 Swarup P, Chen P, Hou R, Que P J, Liu P, Kong A W K. 2021. Giant panda behaviour recognition using images. Global Ecology and Conservation, 26. DOI: 10. 1016/j. gecco. 2021. e01510 Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. 2016. Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (Cvpr). DOI: 10. 1109/Cvpr. 2016. 308:2818-2826 Tobias J A, Planque R, Cram D L, Seddon N. 2014. Species interactions and the structure of complex communication networks. Proceedings of the National Academy of Sciences of the United States of America, 111 (3): 1020-1025. Tran T T K, Bateni S M, Ki S J, Vosoughifar H. 2021. A review of neural networks for air temperature forecasting. Water, 13 (9):1294. Ueno M, Kabata R, Hayashi H, Terada K, Yamada K. 2022. Automatic individual recognition of Japanese macaques (Macaca fuscata) from sequential images. Ethology, 128 (5): 461-470. Venkitasubramanian A N, Tuytelaars T, Moens M F. 2016. Wildlife recognition in nature documentaries with weak supervision from subtitles and external data. Pattern Recognition Letters, 81:63-70. Wang C L, Liu S C, Wang Y W, Xiong J T, Zhang Z G, Zhao B, Luo L F, Lin G C, He P. 2022. Application of convolutional Neural network-based detection methods in fresh fruit production: A comprehensive review. Frontiers in Plant Science, 13. DOI:10. 3389/fpls. 2022. 868745 Wang L, Ding R Z, Zhai Y H, Zhang Q L, Tang W, Zheng N N, Hua G. 2021. Giant Panda Identification. IEEE Transactions on Image Processing, 30: 2837-2849. Wang M, Yin X, Zhu Y, Hu J. 2022. Representation learning and pattern recognition in cognitive biometrics: A survey. Sensors (Basel), 22 (14). DOI: 10. 3390/s22145111 Wang Y H, Su W H. 2022. Convolutional neural networks in computer vision for grain crop phenotyping: A review. AgronomyBasel, 12 (11). DOI: 10. 3390/agronomy12112659 Weinstein B G. 2018. A computer vision for animal ecology. Journal of Animal Ecology, 87 (3): 533-545. Wen B, Zeng W F, Liao Y X, Shi Z, Savage S R, Jiang W, Zhang B. 2020. Deep learning in proteomics. Proteomics, 20 (21-22). DOI: 10. 1002/pmic. 201900335 Westphal A M, Breiter C J C, Falconer S, Saffar N, Ashraf A B, McCall A G, McIver K, Petersen S D. 2022. Citizen science and machine learning: Interdisciplinary approach to non-invasively monitoring a northern marine ecosystem. Frontiers in Marine Science, 9. DOI: 10. 3389/fmars. 2022. 961095 Xie J J, Li A Q, Zhang J G, Cheng Z A. 2019. An integrated wildlife recognition model based on multi-branch aggregation and Squeeze-and-Excitation Network. Applied Sciences-Basel, 9(14). DOI: 10. 3390/app9142794 You M Q. 2020. Changes of China's regulatory regime on commercial artificial breeding of terrestrial wildlife in time of COVID-19 outbreak and impacts on the future. Biological Conservation, 250. DOI: 10. 1016/j. biocon. 2020. 108756 Yu Y, Si X S, Hu C H, Zhang J X. 2019. A review of recurrent neural networks: LSTM cells and Network Architectures. Neural Computation, 31 (7): 1235-1270. Yuan H C, Cai Z Y, Zhou H, Wang Y, Chen X Z. 2021. TransAnomaly: Video anomaly detection using video vision transformer. Ieee Access, 9: 123977-123986. Zafar A, Aamir M, Nawi N M, Arshad A, Riaz S, Alruban A, Dutta A K, Almotairi S. 2022. A comparison of pooling methods for convolutional neural networks. Applied Sciences-Basel, 12 (17). DOI: 10. 3390/app12178643 Zeiler M D, Fergus R. 2014. Visualizing and understanding convolutional Networks. Computer Vision-Eccv 2014, Pt I, 8689:818-833. Zhang T, Liu L C, Zhao K, Wiliem A, Hemson G, Lovell B. 2020. Omni-supervised joint detection and pose estimation for wild animals. Pattern Recognition Letters, 132: 84-90. Zhang X B, Zhai D H, Li T R, Zhou Y X, Lin Y. 2023. Image inpainting based on deep learning: A review. Information Fusion, 90:74-94. Zhao Q J, Zhang Y Q, Hou R, He M N, Liu P, Xu P, Zhang Z H, Chen P. 2022. Automatic recognition of giant panda attributes from their vocalizations based on Squeeze-and-Excitation Network. Sensors, 22 (20). DOI: 10. 3390/s22208015 Zhu W, Xie L X, Han J Y, Guo X Q. 2020. The application of deep learning in cancer prognosis prediction. Cancers, 12 (3). DOI:10. 3390/cancers12030603 丁剑勇, 周阳亮, 许肖梅. 2022. 基于深度学习的鱼群视频轨迹追踪. 中国声学学会水声学分会 2021—2022 年学术会议论文集. 中国山东青岛, 136-139. 马金林, 张裕, 马自萍, 毛凯绩. 2022. 轻量化神经网络卷积设计研究进展. 计算机科学与探索, 16 (3): 512-528. 马海港, 范鹏来. 2023. 被动声学监测技术在陆生哺乳动物研究中的应用、进展和展望. 生物多样性, 31 (1): 32-42. 史春妹, 谢佳君, 顾佳音, 刘丹, 姜广顺. 2021. 基于目标检测的东北虎个体自动识别. 生态学报, 41 (12): 4685-4693. 边疆晖. 2021. 中国兽类种群生态学研究进展与展望. 兽类学报, 41 (5): 556-570. 杨铭伦, 张旭, 郭颖, 于新文, 侯亚男, 高家军. 2022. 基于 YOLOv5的红外相机野生动物图像识别. 激光与光电子学进展, 59 (12):382-390. 肖治术, 肖文宏, 王天明, 李晟, 连新明, 宋大昭, 邓雪琴, 周岐海. 2022. 中国野生动物红外相机监测与研究: 现状及未来. 生物多样性, 30 (10): 234-259. 张同作, 江峰, 徐波, 李斌, 梁程博, 顾海峰. 2022. 青藏高原濒危兽类保护与管理研究进展. 兽类学报, 42 (5): 490-507. 陈宇, 万辉帆, 邹茂扬. 2021. 基于 Wasserstein Gan 的无监督单模配准方法. 南方医科大学学报, 41 (9): 1366-1373. 赵婷婷, 周哲峰, 李东喜, 刘松, 李明. 2018. 基于改进的 Cifar-10 深度学习模型的金钱豹个体识别研究. 太原理工大学学报, 49(4): 585-591, 598. 黄志静, 邵慕义, 张庭瑞, 沈嘉轶. 2022. 基于深度学习的野生动物识别. 电子测试, 36 (22): 69-71, 10. 游剑滢. 2022. 福建省自然保护区国家重点保护陆生野生动物调查. 福建林业, (6): 29-32. |
[1] | Yu QI, Han SU, Rong HOU, Peng LIU, Peng CHEN, Hangxing ZANG, Zhihe ZHANG. Giant panda pose estimation method based on high resolution net [J]. ACTA THERIOLOGICA SINICA, 2022, 42(4): 451-460. |
[2] | GONG Yinan, TAN Mengyu, WANG Zhen, ZHAO Guojing, JIANG Peilin, JIANG Shiming, ZHANG Dingji, GE Jianping, FENG Limin. AI recognition of infrared camera image of wild animals based on deep learning: Northeast Tiger and Leopard National Park for example [J]. ACTA THERIOLOGICA SINICA, 2019, 39(4): 458-465. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||